Name: N S Ramanujam Mangena

We were given a dataset which states who availed Term deposit from the previous campaign. Before we start with analyis, as an every data science project lets establish some basic understanding of the given problem statement.

Initial inference is just 11.7% of the members had availed Term deposit. This suggests that data is highly inbalanced. Now our aim is to build a model to predict the number of people who had actually taken Term deposit. Hence False negatives are vital for model performance and we concentrate on recall to measure the model's performance.

More over this is a classification problem as our target column is 'Target' which is categorical in nature. 1/0(True/False). We use Ensemble methods to build predictive models.

#import basic libraries
import pandas as pd  
import numpy as np  
import matplotlib.pyplot as plt  
import seaborn as sns 
%matplotlib inline
sns.set(style="whitegrid")

import warnings
warnings.filterwarnings("ignore")

bank_df = pd.read_csv("bank-full.csv")
bank_df.shape

(45211, 17)

bank_df2 = bank_df.copy()

bank_df.head()

sum(bank_df.duplicated())

0

Suggests no duplicate rows

bank_df.isnull().sum()

age          0
job          0
marital      0
education    0
default      0
balance      0
housing      0
loan         0
contact      0
day          0
month        0
campaign     0
pdays        0
previous     0
poutcome     0
Target       0
dtype: int64

bank_df.isna().sum()

age          0
job          0
marital      0
education    0
default      0
balance      0
housing      0
loan         0
contact      0
day          0
month        0
campaign     0
pdays        0
previous     0
poutcome     0
Target       0
dtype: int64

suggests that no null vaalues or NaN rows in the dataset.

bank_df.describe()

Five point summary suggest that data is very screwed in all the contineous variables.
pdays has many records with -1 which doesnt seem to be right.
Many accounts have negative balances, could be overdraft accounts.

bank_df.skew().sort_values()

day          0.093079
age          0.684818
pdays        2.615715
duration     3.144318
campaign     4.898650
balance      8.360308
previous    41.846454
dtype: float64

Previous column is highly skewed.

bank_df[bank_df.pdays < 0].pdays.count()

36954

As its very high in quantity, We can consider absolute values of the column for pdays.

bank_df[bank_df.balance < 0].balance.count()

3766

We have 3766 members who have utilised overdraft account.

bank_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 45211 entries, 0 to 45210
Data columns (total 17 columns):
age          45211 non-null int64
job          45211 non-null int32
marital      45211 non-null int32
education    45211 non-null int32
default      45211 non-null int32
balance      45211 non-null int64
housing      45211 non-null int32
loan         45211 non-null int32
contact      45211 non-null int32
day          45211 non-null int64
month        45211 non-null int32
duration     45211 non-null int64
campaign     45211 non-null int64
pdays        45211 non-null int64
previous     45211 non-null int64
poutcome     45211 non-null int32
Target       45211 non-null int32
dtypes: int32(10), int64(7)
memory usage: 4.1 MB

Many columns have the object datatype which cant be used to perform any model building. We will later use Lable Encoder library to convert them to machine readable formats.

bank_df.drop(['duration'],axis =1,inplace = True)

This columns is dropped as it wouldnt be useful in building prediction models and problem statement advised us to ignore this colum as it could lead into inappropriate model building.

Replacing negative pdays values with absolute values

bank_df['pdays'] = bank_df['pdays'].abs()

Displaying the categorical columns summary and how data is distributed for a quick reference.

#Drop duplicate records
bank_df.drop_duplicates(inplace =True)

sum(bank_df.duplicated())

0

bank_df2[['education','job','marital','default','housing','loan','contact','month','poutcome','Target']].apply(lambda x: x.value_counts()).T.stack()

education  primary           6851.0
           secondary        23202.0
           tertiary         13301.0
           unknown           1857.0
job        admin.            5171.0
           blue-collar       9732.0
           entrepreneur      1487.0
           housemaid         1240.0
           management        9458.0
           retired           2264.0
           self-employed     1579.0
           services          4154.0
           student            938.0
           technician        7597.0
           unemployed        1303.0
           unknown            288.0
marital    divorced          5207.0
           married          27214.0
           single           12790.0
default    no               44396.0
           yes                815.0
housing    no               20081.0
           yes              25130.0
loan       no               37967.0
           yes               7244.0
contact    cellular         29285.0
           telephone         2906.0
           unknown          13020.0
month      apr               2932.0
           aug               6247.0
           dec                214.0
           feb               2649.0
           jan               1403.0
           jul               6895.0
           jun               5341.0
           mar                477.0
           may              13766.0
           nov               3970.0
           oct                738.0
           sep                579.0
poutcome   failure           4901.0
           other             1840.0
           success           1511.0
           unknown          36959.0
Target     no               39922.0
           yes               5289.0
dtype: float64

Note:

As unknown values are given as categorical in problem statement, we are treating them as one of the category. They are not equal to NaN where actual data would be missing.

Lable Encoding

We will replace above listed columns with the machine learning format. Here were using lable encoder technique so that we dont create new columns(like in One-Hot encoding). Object columns as well are covereted to integer.

#Lable Encoder to change the categorical values to numerical values.
from sklearn.preprocessing import LabelEncoder
from sklearn import preprocessing

le = preprocessing.LabelEncoder()
bank_df.job = le.fit_transform(bank_df.job)
bank_df.marital = le.fit_transform(bank_df.marital)
bank_df.education = le.fit_transform(bank_df.education)
bank_df.default = le.fit_transform(bank_df.default)
bank_df.housing = le.fit_transform(bank_df.housing)
bank_df.loan = le.fit_transform(bank_df.loan)
bank_df.contact = le.fit_transform(bank_df.contact)
bank_df.month = le.fit_transform(bank_df.month)
bank_df.poutcome = le.fit_transform(bank_df.poutcome)
bank_df.Target = le.fit_transform(bank_df.Target)
bank_df.head()
bank_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 45195 entries, 0 to 45210
Data columns (total 16 columns):
age          45195 non-null int64
job          45195 non-null int32
marital      45195 non-null int32
education    45195 non-null int32
default      45195 non-null int32
balance      45195 non-null int64
housing      45195 non-null int32
loan         45195 non-null int32
contact      45195 non-null int32
day          45195 non-null int64
month        45195 non-null int32
campaign     45195 non-null int64
pdays        45195 non-null int64
previous     45195 non-null int64
poutcome     45195 non-null int32
Target       45195 non-null int32
dtypes: int32(10), int64(6)
memory usage: 4.1 MB

Univariate/Bivariate Analysis

Its clear that below are contineous variables in given dataset.

Age
Balance
Last contacted
Campaign
pdays- though numeric,due to its uneven distribution, unable to plot the details.

Rest of the values are categorical/Nominal/binary We can perform Univariate analysis on these parameters.

plt.figure(figsize=(30,5))
# subplot 1
plt.subplot(1, 5, 1)
plt.title('Customer Age Distribution')
sns.distplot(bank_df.age,color='Red')

# subplot 2
plt.subplot(1, 5, 2)
plt.title('Customer balance')
sns.distplot(bank_df.balance,color='green')

# subplot 3l
plt.subplot(1, 5, 3)
plt.title('Customer last contacted')
sns.distplot(bank_df.day,color='blue')

# subplot 4l
#plt.subplot(1, 5, 4)
#plt.title('Customer last conversation duration')
#sns.distplot(bank_df.duration,color='Orange')

# subplot 5l
plt.subplot(1, 5, 4)
plt.title('Customer campaign Distribution')
sns.distplot(bank_df.campaign,color='brown')


plt.figure(figsize=(30,5))
# subplot 1
plt.subplot(1, 5, 1)
plt.title('Customer Age boxribution')
sns.boxplot(bank_df.age,orient='vertical',color='Red')

# subplot 2
plt.subplot(1, 5, 2)
plt.title('Customer balance')
sns.boxplot(bank_df.balance,orient='vertical',color='green')

# subplot 3l
plt.subplot(1, 5, 3)
plt.title('Customer last contacted')
sns.boxplot(bank_df.day,orient='vertical',color='blue')

# subplot 4l
#plt.subplot(1, 5, 4)
#plt.title('Customer last conversation duration')
#sns.boxplot(bank_df.duration,orient='vertical',color='Orange')

# subplot 5l
plt.subplot(1, 5, 4)
plt.title('Customer campaign distribution')
sns.boxplot(bank_df.campaign,orient='vertical',color='brown')

<matplotlib.axes._subplots.AxesSubplot at 0x1b923692080>

Data is highly skewed and unbalanced. Further inferences to be followed post profiling.

Alternatively, there is a pandas profling library which can be used to analyse each and every attribute in the given dataset.

import pandas_profiling
from pandas_profiling import ProfileReport

ProfileReport(bank_df)

Inferences:¶

Below inferences obtained from pandas profiling aswell.

Data set is higly unblanced dataset and all the variables seems to be weak learners based on the correlation values. Dataset has only 11.7% Target values with Yes.

Majority of the customers have between 20 and 60 years of age. Could be something to do with the retirement age.
balance has records with negative entries, it could be something to do with overdraft accounts/loan accounts. There are some outliers. , we will be trying to remove some outliers.
customer most contacted 20/21st of the month.
Majority of the customers are contacted through cellular phone.
51% customers have Secondary education
There are wide variety of jobs and out of all, blue collared jobs are high in dataset.
60% are married in the given data.
Majority of customer contacts happened in the month of May.
Majority of previous outcome is unknown.
previous is highly skewed (γ1 = 41.846) Skewed
Dataset has 16 duplicate rows post removal of duration column.

plt.title('Target Distribution')
ax=sns.countplot(data = bank_df, x= 'Target',palette='inferno') 
for p in ax.patches: 
    ax.annotate(str((np.round(p.get_height()/len(bank_df)*100,decimals=2)))+'%', (p.get_x()+p.get_width()/2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')

categorcial_variables = ['marital', 'education','default','housing','loan','contact','poutcome','month','job']

plt.figure(figsize=(60,10))
#categorcial_variables = ['job', 'marital', 'education','default','housing','loan','contact','month','poutcome','Target']
j = range(len(categorcial_variables))
for i in j:
    plt.subplot(1, 9, i+1)
    ax=sns.countplot(data = bank_df,palette='inferno', x= categorcial_variables[i]) 
    for p in ax.patches: 
        ax.annotate(str((np.round(p.get_height()/len(bank_df)*100,decimals=2)))+'%', (p.get_x()+p.get_width()/2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')


plt.figure(figsize=(60,10))
#categorcial_variables = ['job', 'marital', 'education','default','housing','loan','contact','month','poutcome','Target']
p = range(len(categorcial_variables))
for i in p:
    plt.subplot(1, 9, i+1)
    ax=sns.countplot(data = bank_df, x= categorcial_variables[i],palette='inferno',hue = 'Target') 
    for p in ax.patches: 
        ax.annotate(str((np.round(p.get_height()/len(bank_df)*100,decimals=2)))+'%', (p.get_x()+p.get_width()/2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')

plt.figure(figsize=(10,4))
sns.barplot(bank_df2['job'].value_counts().values, bank_df2['job'].value_counts().index)
plt.title('job')
plt.tight_layout()

Inferences:¶

Below inferences obtained from pandas profiling aswell.

Data set has a very less polutation where target is market to Yes. (only 11.7 %)
High number of Married members have opted for term deposit.
High number of members with secondary education opted for Term deposit.
Non defaulters have higher %of Term deposits.
Members with no house loan have the Term deposits.
Members who were contacted through Cellular phone holds more Term deposits.
Most people taken Term deposits in the month of May.
People in Management positions hold more term deposits.
In the given dataset, blue collar members are high in number.
There seems to be drop in balance after 60 years.

Normalizing the Data¶

Outliers

Trying to remove the outliers of mortgage with IQR is not helpful as its removing the valid data. Hence some of the outliers are reduced with z score and beyond threshold of 3. We would be removing 745 rows with is close to 2% which is with in permissable limits. Morever we are not worried about balance outliers as current dataset is highly unbalanced and further removing data would impact the predictability of the model.

#import z statistic library
from scipy import stats

bank_df['balance_z'] = np.abs(stats.zscore(bank_df.balance))
bank_df=bank_df[bank_df.balance_z <3]

Remove unwanted columns
Based on the above inferences we are dropping the unwanted columns

bank_df.drop('balance_z', axis = 1, inplace=True)

bank_df.shape

(44450, 16)

# correlation on the given dataset
rs = np.random.RandomState(0)
df = pd.DataFrame(rs.rand(10, 10))
corr = bank_df.corr()
corr.style.background_gradient(cmap='coolwarm')

import seaborn as sns
sns.pairplot(bank_df)

<seaborn.axisgrid.PairGrid at 0x1b91327ff28>

plt.figure(figsize=(30, 6))
plt.subplot(1,6,1)
#sns.jointplot(x = 'Income', y = 'CCAvg', data = loan_df,kind = 'kde')
#plt.subplot(1,4,2)
plt.title('Age vs Target')
sns.boxplot(x = 'age', y = 'Target',orient="horizontal",data = bank_df)
plt.subplot(1,6,2)
plt.title('Balance vs Target')
sns.boxplot(x = 'balance', y = 'Target',orient="horizontal", data = bank_df)

<matplotlib.axes._subplots.AxesSubplot at 0x1b92d2ed278>

Inferences:¶

Inspite of clearing the outliers, couldnt notice much improvement with the outliers and above correlation matrix suggests very weak relation. Though we have weak learners we try to make use of them and try to use ensemble learning to improve the predictions.

Positively correlated columns

pdays
previous
balance
education
marital
job
age

Negatively correlated columns

default
month
day
loan
campaign
poutcome
housing
contact

Split Target columns and Independendent columns¶

Though columns have negative correlation and loosely corelated, we are not removing them as weak learners contribute for building decision trees and ensemble methods.

#Train and Test data split library import
from sklearn.model_selection import train_test_split

X = bank_df.drop(['Target'],axis=1) # independant variables
y = bank_df['Target'] #Dependant variables

Split Data to 70:30 Train and Test ratio

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10)

print("Original Target True Values    : {0} ({1:0.2f}%)".format(len(bank_df.loc[bank_df['Target'] == 1]), (len(bank_df.loc[bank_df['Target'] == 1])/len(bank_df.index)) * 100))
print("Original Target False Values   : {0} ({1:0.2f}%)".format(len(bank_df.loc[bank_df['Target'] == 0]), (len(bank_df.loc[bank_df['Target'] == 0])/len(bank_df.index)) * 100))
print("")
print("Training Target True Values    : {0} ({1:0.2f}%)".format(len(y_train[y_train[:] == 1]), (len(y_train[y_train[:] == 1])/len(y_train)) * 100))
print("Training Target False Values   : {0} ({1:0.2f}%)".format(len(y_train[y_train[:] == 0]), (len(y_train[y_train[:] == 0])/len(y_train)) * 100))
print("")
print("Test Target True Values        : {0} ({1:0.2f}%)".format(len(y_test[y_test[:] == 1]), (len(y_test[y_test[:] == 1])/len(y_test)) * 100))
print("Test Target False Values       : {0} ({1:0.2f}%)".format(len(y_test[y_test[:] == 0]), (len(y_test[y_test[:] == 0])/len(y_test)) * 100))
print("")

Original Target True Values    : 5168 (11.63%)
Original Target False Values   : 39282 (88.37%)

Training Target True Values    : 3629 (11.66%)
Training Target False Values   : 27486 (88.34%)

Test Target True Values        : 1539 (11.54%)
Test Target False Values       : 11796 (88.46%)

Lets predict using models!!¶

Logistic Regression¶

#Import required metrics libraries
from sklearn import metrics
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve

#Logistic regression model import
from sklearn.linear_model import LogisticRegression

#importing cross validation and Grid search library
from sklearn.model_selection import GridSearchCV

Hyper tuning parameters with the help of GridsearchCV

grid={"C":np.logspace(-3,3,7), "penalty":["l1","l2"]}# l1 lasso l2 ridge
model=LogisticRegression(max_iter=50000)
model_cv=GridSearchCV(model,grid,cv=10)
model_cv.fit(X_train,y_train)

print("Tuned hpyerparameters :(best parameters) ",model_cv.best_params_)
print("Accuracy :",model_cv.best_score_)

Tuned hpyerparameters :(best parameters)  {'C': 1000.0, 'penalty': 'l2'}
Accuracy : 0.8824951687946638

# Fit the model with the help of hyper parameters

model2=LogisticRegression(C=1000,penalty="l2")
#model2=LogisticRegression()
model2.fit(X_train,y_train)

LogisticRegression(C=1000, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)

#predict on test
y_pred = model2.predict(X_test)

print('\033[91m' + "Logistic Regression Stats" + '\033[0m')
print(classification_report(y_test,y_pred))
print("Recall : ",round(recall_score(y_test,y_pred),2))
print("Accuracy : ",round(accuracy_score(y_test,y_pred),2))
print("Precision : ",round(precision_score(y_test,y_pred),2))

print('\033[91m' + "Logistic Regression Confusion Matrix" + '\033[0m')
#print(confusion_matrix(y_test,y_pred))
mcm=confusion_matrix(y_test, y_pred, labels=[1, 0])

log_df_cm = pd.DataFrame(mcm, index = [i for i in ["1","0"]],
                  columns = [i for i in ["Predict 1","Predict 0"]])
log_df_cm

#coef_df = pd.DataFrame(model.coef_)
#coef_df['intercept'] = model.intercept_
#print(coef_df)

Logistic Regression Stats
              precision    recall  f1-score   support

           0       0.88      1.00      0.94     11796
           1       0.27      0.00      0.00      1539

    accuracy                           0.88     13335
   macro avg       0.58      0.50      0.47     13335
weighted avg       0.81      0.88      0.83     13335

Recall :  0.0
Accuracy :  0.88
Precision :  0.27
Logistic Regression Confusion Matrix

ROC/AUC Curve

log_roc_auc = roc_auc_score(y_test, model2.predict(X_test))
fpr, tpr, thresholds = roc_curve(y_test, model2.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='Logistic Regression (area = %02f)' % log_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.savefig('Log_ROC')
plt.show()

Naive Bayes¶

# To model the Gaussian Navie Bayes classifier
from sklearn.naive_bayes import GaussianNB

nb_clf = GaussianNB()
NB_result = nb_clf.fit(X_train, y_train)
nb_clf.fit(X_train, y_train)

GaussianNB(priors=None, var_smoothing=1e-09)

y_pred = nb_clf.predict(X_test)

print('\033[91m' + "Naive Bayes Stats" + '\033[0m')
print(classification_report(y_test,y_pred))
print("Recall : ",round(recall_score(y_test,y_pred),2))
print("Accuracy : ",round(accuracy_score(y_test,y_pred),2))
print("Precision : ",round(precision_score(y_test,y_pred),2))

print('\033[91m' + "Naive Bayes Confusion Matrix" + '\033[0m')
#print(confusion_matrix(y_test,y_pred))
nv_mcm=confusion_matrix(y_test, y_pred, labels=[1, 0])

nv_df_cm = pd.DataFrame(nv_mcm, index = [i for i in ["1","0"]],
                  columns = [i for i in ["Predict 1","Predict 0"]])
nv_df_cm

Naive Bayes Stats
              precision    recall  f1-score   support

           0       0.91      0.86      0.89     11796
           1       0.25      0.35      0.29      1539

    accuracy                           0.80     13335
   macro avg       0.58      0.61      0.59     13335
weighted avg       0.83      0.80      0.82     13335

Recall :  0.35
Accuracy :  0.8
Precision :  0.25
Naive Bayes Confusion Matrix

ROC/AUC Curve

NB_roc_auc = roc_auc_score(y_test,nb_clf.predict(X_test))
fpr, tpr, thresholds = roc_curve(y_test, nb_clf.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='NB Classifier (area = %2f)' % NB_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.savefig('NB_ROC')
plt.show()

KNN-Classification¶

With KNN we deal with neighbouring data points. There is no specific criteria to choose the K value. Generally we start with neighbours of 3,5,7 etc., However when calculate accuracy score is reducing when calculated with 5 and 7. Hence we have confined to the test with k = 3.

# loading library
from sklearn.neighbors import KNeighborsClassifier

# instantiate learning model (k = 3)
knn = KNeighborsClassifier(n_neighbors = 3)

# fitting the model
knn.fit(X_train, y_train)
# predict the response
y_pred = knn.predict(X_test)

print('\033[91m' + "KNN Stats" + '\033[0m')
print(classification_report(y_test,y_pred))
print("Recall : ",round(recall_score(y_test,y_pred),2))
print("Accuracy : ",round(accuracy_score(y_test,y_pred),2))
print("Precision : ",round(precision_score(y_test,y_pred),2))

print('\033[91m' + "KNN Confusion Matrix" + '\033[0m')
#print(confusion_matrix(y_test,y_pred))
knn_mcm=confusion_matrix(y_test, y_pred, labels=[1, 0])

knn_df_cm = pd.DataFrame(knn_mcm, index = [i for i in ["1","0"]],
                  columns = [i for i in ["Predict 1","Predict 0"]])
knn_df_cm

KNN Stats
              precision    recall  f1-score   support

           0       0.90      0.96      0.93     11796
           1       0.33      0.14      0.20      1539

    accuracy                           0.87     13335
   macro avg       0.61      0.55      0.56     13335
weighted avg       0.83      0.87      0.84     13335

Recall :  0.14
Accuracy :  0.87
Precision :  0.33
KNN Confusion Matrix

KNN_roc_auc = roc_auc_score(y_test,knn.predict(X_test))
fpr, tpr, thresholds = roc_curve(y_test, knn.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='KNN Classifier (area = %2f)' % KNN_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.savefig('NB_ROC')
plt.show()

Decision Tree¶

from sklearn.tree import DecisionTreeClassifier
#from sklearn.feature_extraction.text import CountVectorizer  #DT does not take strings as input for the model fit step....
from IPython.display import Image  
#import pydotplus as pydot
from sklearn import tree
from os import system

from graphviz import Source

dTree = DecisionTreeClassifier(criterion = 'entropy')
dTree.fit(X_train, y_train)

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',
                       max_depth=None, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort='deprecated',
                       random_state=None, splitter='best')

print(dTree.score(X_train, y_train))
print(dTree.score(X_test, y_test))

1.0
0.833970753655793

Decisions trees are prone to overfit as in this case. Hence we implement pruning of branches. Lets see that further in regularization

train_char_label = ['1', '0']
Credit_Tree_File = open('credit_tree.dot','w')
dot_data = tree.export_graphviz(dTree, out_file=Credit_Tree_File, feature_names = list(X_train), class_names = list(train_char_label))
Credit_Tree_File.close()

retCode = system("dot -Tpng credit_tree.dot -o credit_tree.png")
if(retCode>0):
    print("system command returning error: "+str(retCode))
else:
    display(Image("credit_tree.png"))

    # importance of features in the tree building ( The importance of a feature is computed as the 
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print(pd.DataFrame(dTree.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values("Imp", ascending= False))

                Imp
balance    0.236831
age        0.148846
day        0.127088
month      0.087488
campaign   0.068933
job        0.067702
pdays      0.061334
contact    0.044555
poutcome   0.042421
education  0.037424
housing    0.024167
marital    0.022563
loan       0.014049
previous   0.014037
default    0.002561

y_pred = dTree.predict(X_test)

print('\033[91m' + "Decision Tree Stats" + '\033[0m')
print(classification_report(y_test,y_pred))
print("Recall : ",round(recall_score(y_test,y_pred),2))
print("Accuracy : ",round(accuracy_score(y_test,y_pred),2))
print("Precision : ",round(precision_score(y_test,y_pred),2))

print('\033[91m' + "Decision Tree Confusion Matrix" + '\033[0m')
#print(confusion_matrix(y_test,y_pred))
dt_mcm=confusion_matrix(y_test, y_pred, labels=[1, 0])

dt_df_cm = pd.DataFrame(dt_mcm, index = [i for i in ["1","0"]],
                  columns = [i for i in ["Predict 1","Predict 0"]])
dt_df_cm

Decision Tree Stats
              precision    recall  f1-score   support

           0       0.91      0.90      0.91     11796
           1       0.30      0.33      0.32      1539

    accuracy                           0.83     13335
   macro avg       0.61      0.62      0.61     13335
weighted avg       0.84      0.83      0.84     13335

Recall :  0.33
Accuracy :  0.83
Precision :  0.3
Decision Tree Confusion Matrix

ROC/AUC Curve

DT_roc_auc = roc_auc_score(y_test,dTree.predict(X_test))
fpr, tpr, thresholds = roc_curve(y_test, dTree.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='DTree Classifier (area = %2f)' % DT_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic') 
plt.legend(loc="lower right")
plt.savefig('DT_ROC')
plt.show()

Regularized Decision Tree¶

Lets restrict the depth to 7 and rerun decision tree

reg_dt_model = DecisionTreeClassifier(criterion = 'entropy', max_depth = 7)
reg_dt_model.fit(X_train, y_train)

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',
                       max_depth=7, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort='deprecated',
                       random_state=None, splitter='best')

print(reg_dt_model.score(X_train, y_train))
print(reg_dt_model.score(X_test, y_test))

0.8966414912421662
0.8948631421072366

train_char_label = ['1', '0']
reg_dt_model_File = open('reg_dt_model.dot','w')
dot_data = tree.export_graphviz(reg_dt_model, out_file=reg_dt_model_File, feature_names = list(X_train), class_names = list(train_char_label))
reg_dt_model_File.close()

retCode = system("dot -Tpng reg_dt_model.dot -o reg_dt_model_File.png")
if(retCode>0):
    print("system command returning error: "+str(retCode))
else:
    display(Image("reg_dt_model_File.png"))

    # importance of features in the tree building ( The importance of a feature is computed as the 
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print(pd.DataFrame(reg_dt_model.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values("Imp", ascending= False))

                Imp
poutcome   0.212633
contact    0.199041
month      0.154775
age        0.120007
pdays      0.116148
housing    0.061687
balance    0.046497
marital    0.036825
day        0.031762
campaign   0.008774
education  0.007323
previous   0.003061
job        0.001466
default    0.000000
loan       0.000000

y_pred = reg_dt_model.predict(X_test)

print('\033[91m' + "Regularized Decision Tree Stats" + '\033[0m')
print(classification_report(y_test,y_pred))
print("Recall : ",round(recall_score(y_test,y_pred),2))
print("Accuracy : ",round(accuracy_score(y_test,y_pred),2))
print("Precision : ",round(precision_score(y_test,y_pred),2))

print('\033[91m' + "Regularized Decision Tree Confusion Matrix" + '\033[0m')
#print(confusion_matrix(Regularized y_test,y_pred))
reg_dt_mcm=confusion_matrix(y_test, y_pred, labels=[1, 0])

reg_dt_df_cm = pd.DataFrame(reg_dt_mcm, index = [i for i in ["1","0"]],
                  columns = [i for i in ["Predict 1","Predict 0"]])
reg_dt_df_cm

Regularized Decision Tree Stats
              precision    recall  f1-score   support

           0       0.90      0.99      0.94     11796
           1       0.66      0.18      0.29      1539

    accuracy                           0.89     13335
   macro avg       0.78      0.59      0.61     13335
weighted avg       0.87      0.89      0.87     13335

Recall :  0.18
Accuracy :  0.89
Precision :  0.66
Regularized Decision Tree Confusion Matrix

DT_roc_auc = roc_auc_score(y_test,reg_dt_model.predict(X_test))
fpr, tpr, thresholds = roc_curve(y_test, reg_dt_model.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='Regularized DTree Classifier (area = %2f)' % DT_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic') 
plt.legend(loc="lower right")
plt.savefig('Reg_DT_ROC')
plt.show()

Ensemble Learning - Bagging¶

Bagging with the decision tree as base model

from sklearn.ensemble import BaggingClassifier

bgcl = BaggingClassifier(base_estimator=dTree, n_estimators=50)

#bgcl = BaggingClassifier(n_estimators=50)
bgcl = bgcl.fit(X_train, y_train)

print(bgcl.score(X_train, y_train))
print(bgcl.score(X_test, y_test))

0.9993250843644544
0.8948631421072366

y_pred = bgcl.predict(X_test)

print('\033[91m' + "Bagging Stats" + '\033[0m')
print(classification_report(y_test,y_pred))
print("Recall : ",round(recall_score(y_test,y_pred),2))
print("Accuracy : ",round(accuracy_score(y_test,y_pred),2))
print("Precision : ",round(precision_score(y_test,y_pred),2))

print('\033[91m' + "Bagging Confusion Matrix" + '\033[0m')
#print(confusion_matrix(y_test,y_pred))
bagg_mcm=confusion_matrix(y_test, y_pred, labels=[1, 0])

bagg_mcm_df_cm = pd.DataFrame(bagg_mcm, index = [i for i in ["1","0"]],
                  columns = [i for i in ["Predict 1","Predict 0"]])
bagg_mcm_df_cm

Bagging Stats
              precision    recall  f1-score   support

           0       0.91      0.98      0.94     11796
           1       0.61      0.25      0.35      1539

    accuracy                           0.89     13335
   macro avg       0.76      0.61      0.65     13335
weighted avg       0.87      0.89      0.87     13335

Recall :  0.25
Accuracy :  0.89
Precision :  0.61
Bagging Confusion Matrix

bgcl_roc_auc = roc_auc_score(y_test,bgcl.predict(X_test))
fpr, tpr, thresholds = roc_curve(y_test, bgcl.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='Bagging Classifier (area = %2f)' % bgcl_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic') 
plt.legend(loc="lower right")
plt.savefig('bgcl_ROC')
plt.show()

Ensemble Learning - AdaBoosting¶

from sklearn.ensemble import AdaBoostClassifier
abcl = AdaBoostClassifier(base_estimator=dTree, n_estimators=10)
#abcl = AdaBoostClassifier( n_estimators=50)
abcl = abcl.fit(X_train, y_train)

print(abcl.score(X_train, y_train))
print(abcl.score(X_test, y_test))

1.0
0.8372703412073491

y_pred = abcl.predict(X_test)

print('\033[91m' + "Adaboost Stats" + '\033[0m')
print(classification_report(y_test,y_pred))
print("Recall : ",round(recall_score(y_test,y_pred),2))
print("Accuracy : ",round(accuracy_score(y_test,y_pred),2))
print("Precision : ",round(precision_score(y_test,y_pred),2))

print('\033[91m' + "Adaboost Confusion Matrix" + '\033[0m')
#print(confusion_matrix(y_test,y_pred))
abcl_mcm=confusion_matrix(y_test, y_pred, labels=[1, 0])

abcl_mcm_df_cm = pd.DataFrame(abcl_mcm, index = [i for i in ["1","0"]],
                  columns = [i for i in ["Predict 1","Predict 0"]])
abcl_mcm_df_cm

Adaboost Stats
              precision    recall  f1-score   support

           0       0.91      0.90      0.91     11796
           1       0.31      0.33      0.32      1539

    accuracy                           0.84     13335
   macro avg       0.61      0.62      0.61     13335
weighted avg       0.84      0.84      0.84     13335

Recall :  0.33
Accuracy :  0.84
Precision :  0.31
Adaboost Confusion Matrix

abcl_roc_auc = roc_auc_score(y_test,abcl.predict(X_test))
fpr, tpr, thresholds = roc_curve(y_test, abcl.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='Adaboost Classifier (area = %2f)' % abcl_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic') 
plt.legend(loc="lower right")
plt.savefig('abcl_ROC')
plt.show()

Ensemble Learning - GradientBoost¶

from sklearn.ensemble import GradientBoostingClassifier
gbcl = GradientBoostingClassifier(n_estimators = 50)
gbcl = gbcl.fit(X_train, y_train)

print(gbcl.score(X_train, y_train))
print(gbcl.score(X_test, y_test))

0.8934276072633778
0.8943382077240345

y_pred = gbcl.predict(X_test)

print('\033[91m' + "Gradient Boost Stats" + '\033[0m')
print(classification_report(y_test,y_pred))
print("Recall : ",round(recall_score(y_test,y_pred),2))
print("Accuracy : ",round(accuracy_score(y_test,y_pred),2))
print("Precision : ",round(precision_score(y_test,y_pred),2))

print('\033[91m' + "Gradient Boost Confusion Matrix" + '\033[0m')
#print(confusion_matrix(y_test,y_pred))
gbcl_mcm=confusion_matrix(y_test, y_pred, labels=[1, 0])

gbcl_mcm_df_cm = pd.DataFrame(gbcl_mcm, index = [i for i in ["1","0"]],
                  columns = [i for i in ["Predict 1","Predict 0"]])
gbcl_mcm_df_cm

Gradient Boost Stats
              precision    recall  f1-score   support

           0       0.90      0.99      0.94     11796
           1       0.69      0.15      0.25      1539

    accuracy                           0.89     13335
   macro avg       0.80      0.57      0.60     13335
weighted avg       0.88      0.89      0.86     13335

Recall :  0.15
Accuracy :  0.89
Precision :  0.69
Decision Tree Confusion Matrix

GB_roc_auc = roc_auc_score(y_test,gbcl.predict(X_test))
fpr, tpr, thresholds = roc_curve(y_test, gbcl.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='Gradient Boost Classifier (area = %2f)' % GB_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic') 
plt.legend(loc="lower right")
plt.savefig('GB_ROC')
plt.show()

Ensemble Learning - RandomForest¶

from sklearn.ensemble import RandomForestClassifier
rfcl = RandomForestClassifier(n_estimators = 50)
rfcl = rfcl.fit(X_train, y_train)

print(rfcl.score(X_train, y_train))
print(rfcl.score(X_test, y_test))

0.9991643901655151
0.8936632920884889

y_pred = rfcl.predict(X_test)

print('\033[91m' + "Random Forest Stats" + '\033[0m')
print(classification_report(y_test,y_pred))
print("Recall : ",round(recall_score(y_test,y_pred),2))
print("Accuracy : ",round(accuracy_score(y_test,y_pred),2))
print("Precision : ",round(precision_score(y_test,y_pred),2))

print('\033[91m' + "Random Forest Confusion Matrix" + '\033[0m')
#print(confusion_matrix(y_test,y_pred))
rfcl_mcm=confusion_matrix(y_test, y_pred, labels=[1, 0])

rfcl_mcm_df_cm = pd.DataFrame(rfcl_mcm, index = [i for i in ["1","0"]],
                  columns = [i for i in ["Predict 1","Predict 0"]])
rfcl_mcm_df_cm

Random Forest Stats
              precision    recall  f1-score   support

           0       0.91      0.98      0.94     11796
           1       0.61      0.21      0.31      1539

    accuracy                           0.89     13335
   macro avg       0.76      0.60      0.63     13335
weighted avg       0.87      0.89      0.87     13335

Recall :  0.21
Accuracy :  0.89
Precision :  0.61
Random Forest Confusion Matrix

RF_roc_auc = roc_auc_score(y_test,rfcl.predict(X_test))
fpr, tpr, thresholds = roc_curve(y_test, rfcl.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='Randomforest Classifier (area = %2f)' % RF_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic') 
plt.legend(loc="lower right")
plt.savefig('RF_ROC')
plt.show()

Conclusions:¶

Dataset is highly unbalanced and have very weak relation between the features. As we are restricting it to ensemble methods, none of the weakly correlated were excluded. This can be further performed in the feature selection in PCA/LDA techniques. Recall values are as well very low however we have obtained better results with ensemble learning.

Based on ROC/AUC curve below is the order of model performance based on False Negatives and Recall values.

Adboosting with decision tree (Recall= .33) > Bagging with decison tree (Recall = .25) > Regularized decision tree with the depth of 7(Recall = 0.18)

However there is still scope in improving this with the help of feature selection.

	age	balance	day	duration	campaign	pdays	previous
count	45211.000000	45211.000000	45211.000000	45211.000000	45211.000000	45211.000000	45211.000000
mean	40.936210	1362.272058	15.806419	258.163080	2.763841	40.197828	0.580323
std	10.618762	3044.765829	8.322476	257.527812	3.098021	100.128746	2.303441
min	18.000000	-8019.000000	1.000000	0.000000	1.000000	-1.000000	0.000000
25%	33.000000	72.000000	8.000000	103.000000	1.000000	-1.000000	0.000000
50%	39.000000	448.000000	16.000000	180.000000	2.000000	-1.000000	0.000000
75%	48.000000	1428.000000	21.000000	319.000000	3.000000	-1.000000	0.000000
max	95.000000	102127.000000	31.000000	4918.000000	63.000000	871.000000	275.000000

Number of variables	17
Number of observations	45195
Total Missing (%)	0.0%
Total size in memory	4.1 MiB
Average record size in memory	96.0 B

Numeric	13
Categorical	0
Boolean	4
Date	0
Text (Unique)	0
Rejected	0
Unsupported	0

Distinct count	2
Unique (%)	0.0%
Missing (%)	0.0%
Missing (n)	0

Distinct count	77
Unique (%)	0.2%
Missing (%)	0.0%
Missing (n)	0
Infinite (%)	0.0%
Infinite (n)	0

Overview

Variables

Correlations

Sample

Inferences:¶

Inferences:¶

Normalizing the Data¶

Inferences:¶

Split Target columns and Independendent columns¶

Lets predict using models!!¶

Logistic Regression¶

Naive Bayes¶

KNN-Classification¶

Decision Tree¶

Regularized Decision Tree¶

Ensemble Learning - Bagging¶

Ensemble Learning - AdaBoosting¶

Ensemble Learning - GradientBoost¶

Ensemble Learning - RandomForest¶

Conclusions:¶

	age	job	marital	education	default	balance	housing	loan	contact	day	month	duration	campaign	pdays	poutcome	Target
0	58	management	married	tertiary	no	2143	yes	no	unknown	5	may	261	1	-1	unknown	no
1	44	technician	single	secondary	no	29	yes	no	unknown	5	may	151	1	-1	unknown	no
2	33	entrepreneur	married	secondary	no	2	yes	yes	unknown	5	may	76	1	-1	unknown	no
3	47	blue-collar	married	unknown	no	1506	yes	no	unknown	5	may	92	1	-1	unknown	no
4	33	unknown	single	unknown	no	1	no	no	unknown	5	may	198	1	-1	unknown	no

Minimum	18
5-th percentile	27
Q1	33
Median	39
Q3	48
95-th percentile	59
Maximum	95
Range	77
Interquartile range	15

Standard deviation	10.619
Coef of variation	0.2594
Kurtosis	0.31953
Mean	40.938
MAD	8.7374
Skewness	0.68468
Sum	1850175
Variance	112.77
Memory size	353.2 KiB

Value	Count	Frequency (%)
32	2082	4.6%
31	1995	4.4%
33	1971	4.4%
34	1928	4.3%
35	1893	4.2%
36	1806	4.0%
30	1755	3.9%
37	1696	3.8%
39	1487	3.3%
38	1466	3.2%
Other values (67)	27116	60.0%

Distinct count	7168
Unique (%)	15.9%
Missing (%)	0.0%
Missing (n)	0
Infinite (%)	0.0%
Infinite (n)	0

Minimum	-8019
5-th percentile	-172
Q1	72
Median	449
Q3	1428
95-th percentile	5768.3
Maximum	102127
Range	110146
Interquartile range	1356

Standard deviation	3045.2
Coef of variation	2.2346
Kurtosis	140.72
Mean	1362.8
MAD	1551.8
Skewness	8.3593
Sum	61589682
Variance	9273200
Memory size	353.2 KiB

Distinct count	48
Unique (%)	0.1%
Missing (%)	0.0%
Missing (n)	0
Infinite (%)	0.0%
Infinite (n)	0

Standard deviation	3.0983
Coef of variation	1.121
Kurtosis	39.248
Mean	2.764
MAD	1.7916
Skewness	4.8986
Sum	124918
Variance	9.5995
Memory size	353.2 KiB

Standard deviation	0.89799
Coef of variation	1.4023
Kurtosis	-1.3171
Mean	0.64038
MAD	0.8295
Skewness	0.76904
Sum	28942
Variance	0.80639
Memory size	353.2 KiB

0	39906
1	5289

Value	Count	Frequency (%)
0	3498	7.7%
1	195	0.4%
2	156	0.3%
4	139	0.3%
3	134	0.3%
5	113	0.3%
6	88	0.2%
8	81	0.2%
23	75	0.2%
10	69	0.2%
Other values (7158)	40647	89.9%

Value	Count	Frequency (%)
1	17539	38.8%
2	12497	27.7%
3	5520	12.2%
4	3521	7.8%
5	1764	3.9%
6	1291	2.9%
7	735	1.6%
8	540	1.2%
9	327	0.7%
10	265	0.6%
Other values (38)	1196	2.6%

Value	Count	Frequency (%)
1	17539	38.8%
2	12497	27.7%
3	5520	12.2%
4	3521	7.8%
5	1764	3.9%

Standard deviation	8.3228
Coef of variation	0.5266
Kurtosis	-1.0598
Mean	15.805
MAD	7.0561
Skewness	0.093451
Sum	714299
Variance	69.269
Memory size	353.2 KiB

Standard deviation	0.74797
Coef of variation	0.61072
Kurtosis	-0.26314
Mean	1.2247
MAD	0.60187
Skewness	0.19771
Sum	55352
Variance	0.55946
Memory size	353.2 KiB

Minimum	0
5-th percentile	2259.7
Q1	11300
Median	22610
Q3	33912
95-th percentile	42950
Maximum	45210
Range	45210
Interquartile range	22611

Standard deviation	13053
Coef of variation	0.5774
Kurtosis	-1.2004
Mean	22606
MAD	11305
Skewness	-0.00016898
Sum	1021695847
Variance	170380000
Memory size	353.2 KiB

Standard deviation	3.2728
Coef of variation	0.75421
Kurtosis	-1.2704
Mean	4.3394
MAD	2.8003
Skewness	0.26189
Sum	196120
Variance	10.711
Memory size	353.2 KiB

Standard deviation	0.60821
Coef of variation	0.52092
Kurtosis	-0.43943
Mean	1.1676
MAD	0.47078
Skewness	-0.10264
Sum	52768
Variance	0.36992
Memory size	353.2 KiB

Standard deviation	3.0066
Coef of variation	0.54427
Kurtosis	-0.99412
Mean	5.524
MAD	2.5489
Skewness	-0.48083
Sum	249659
Variance	9.0394
Memory size	353.2 KiB

Standard deviation	99.471
Coef of variation	2.377
Kurtosis	7.0238
Mean	41.847
MAD	67.045
Skewness	2.6272
Sum	1891276
Variance	9894.6
Memory size	353.2 KiB

Standard deviation	0.9892
Coef of variation	0.38643
Kurtosis	2.1507
Mean	2.5598
MAD	0.71962
Skewness	-1.973
Sum	115691
Variance	0.97852
Memory size	353.2 KiB

Standard deviation	2.3038
Coef of variation	3.9685
Kurtosis	4505.5
Mean	0.58053
MAD	0.94894
Skewness	41.84
Sum	26237
Variance	5.3076
Memory size	353.2 KiB

	age	job	marital	education	default	balance	housing	loan	contact	day	month	campaign	pdays	previous	poutcome	Target
age	1	-0.0212915	-0.402511	-0.107906	-0.0172308	0.104814	-0.183881	-0.0138414	0.0268168	-0.00851462	-0.0427404	0.00560666	-0.0238788	0.00112105	0.00726906	0.0246699
job	-0.0212915	1	0.0614741	0.166598	-0.00676924	0.0237693	-0.126738	-0.0324384	-0.0817585	0.0226801	-0.0935328	0.00762933	-0.0247756	-0.00149045	0.0116864	0.0398955
marital	-0.402511	0.0614741	1	0.109227	-0.00716488	0.00615874	-0.0160977	-0.0462285	-0.0391954	-0.00581688	-0.00661334	-0.010051	0.0191312	0.0148948	-0.0170023	0.0450142
education	-0.107906	0.166598	0.109227	1	-0.00989407	0.0533001	-0.0907099	-0.0472705	-0.11024	0.0226284	-0.0588634	0.00676742	-0.000918769	0.0171298	-0.0188384	0.0656319
default	-0.0172308	-0.00676924	-0.00716488	-0.00989407	1	-0.0978026	-0.00670588	0.0761787	0.0155268	0.00969652	0.0116192	0.016868	-0.0300713	-0.018336	0.0350933	-0.0222727
balance	0.104814	0.0237693	0.00615874	0.0533001	-0.0978026	1	-0.0673971	-0.101819	-0.0322571	0.0105426	0.0232353	-0.0233535	0.0126636	0.0297823	-0.041691	0.0737594
housing	-0.183881	-0.126738	-0.0160977	-0.0907099	-0.00670588	-0.0673971	1	0.0394599	0.189118	-0.0291133	0.27294	-0.0232304	0.125472	0.0372654	-0.100228	-0.138913
loan	-0.0138414	-0.0324384	-0.0462285	-0.0472705	0.0761787	-0.101819	0.0394599	1	-0.0116121	0.0120939	0.0226674	0.0102279	-0.0226589	-0.0110205	0.015134	-0.0679293
contact	0.0268168	-0.0817585	-0.0391954	-0.11024	0.0155268	-0.0322571	0.189118	-0.0116121	1	-0.0279288	0.364108	0.0192746	-0.244626	-0.1475	0.27273	-0.148001
day	-0.00851462	0.0226801	-0.00581688	0.0226284	0.00969652	0.0105426	-0.0291133	0.0120939	-0.0279288	1	-0.00910897	0.163742	-0.094051	-0.0520433	0.0845149	-0.0292021
month	-0.0427404	-0.0935328	-0.00661334	-0.0588634	0.0116192	0.0232353	0.27294	0.0226674	0.364108	-0.00910897	1	-0.108852	0.0335276	0.0230326	-0.0335514	-0.0233314
campaign	0.00560666	0.00762933	-0.010051	0.00676742	0.016868	-0.0233535	-0.0232304	0.0102279	0.0192746	0.163742	-0.108852	1	-0.0887968	-0.0324218	0.102007	-0.0734474
pdays	-0.0238788	-0.0247756	0.0191312	-0.000918769	-0.0300713	0.0126636	0.125472	-0.0226589	-0.244626	-0.094051	0.0335276	-0.0887968	1	0.452673	-0.857547	0.102271
previous	0.00112105	-0.00149045	0.0148948	0.0171298	-0.018336	0.0297823	0.0372654	-0.0110205	-0.1475	-0.0520433	0.0230326	-0.0324218	0.452673	1	-0.488288	0.0925213
poutcome	0.00726906	0.0116864	-0.0170023	-0.0188384	0.0350933	-0.041691	-0.100228	0.015134	0.27273	0.0845149	-0.0335514	0.102007	-0.857547	-0.488288	1	-0.0773249
Target	0.0246699	0.0398955	0.0450142	0.0656319	-0.0222727	0.0737594	-0.138913	-0.0679293	-0.148001	-0.0292021	-0.0233314	-0.0734474	0.102271	0.0925213	-0.0773249	1